Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Influence of Olive Fruit Storage in Bags on Oil Quality and Composition of Volatile Compounds

The composition of olive oil volatile components depends on genetic factors, ripening grade of the fruit, fruit storage and processing conditions. Storage of olives in reticular or plastic bags is still a frequently used practice that has negative effects on oil quality, particularly on sensory characteristics. The changes of volatile compounds during this procedure were determined using headspace solid phase microextraction (HS-SPME). The method was optimised as regards sample conditioning and extraction time, and verified by testing the repeatability and linearity of the response. The main changes during fruit storage in bags are increase of methanol and ethanol concentration and decrease of 1-penten- 3-one, trans-2-hexenal and cis-3-hexenyl acetate concentration. The changes in plastic bags are more evident and significant differences between the two types of storage are established

Understanding ProbLog as probabilistic argumentation

ProbLog is a popular probabilistic logic programming language and tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper we study some connections between ProbLog and a variant of another well-known formalism combining symbolic reasoning and reasoning under uncertainty, namely probabilistic argumentation. Specifically, we show that ProbLog is an instance of a form of Probabilistic Abstract Argumentation (PAA) under the constellation approach, which builds upon Assumption-Based Argumentation (ABA). The connections pave the way towards equipping ProbLog with a variety of alternative semantics, inherited from PAA/PABA, as well as obtaining novel argumentation semantics for PAA/PABA, leveraging on existing connections between ProbLog and argumentation. Moreover, the connections pave the way towards novel forms of argumentative explanations for ProbLog’s outputs

Efficient and Settings-Free Calibration of Detailed Kinetic Metabolic Models with Enzyme Isoforms Characterization

Mathematical modeling and computational analyses are essential tools to understand and gain novel insights on the functioning of complex biochemical systems. In the specific case of metabolic reaction networks, which are regulated by many other intracellular processes, various challenging problems hinder the definition of compact and fully calibrated mathematical models, as well as the execution of computationally efficient analyses of their emergent dynamics. These problems especially occur when the model explicitly takes into account the presence and the effect of different isoforms of metabolic enzymes. Since the kinetic characterization of the different isoforms is most of the times unavailable, Parameter Estimation (PE) procedures are typically required to properly calibrate the model. To address these issues, in this work we combine the descriptive power of Stochastic Symmetric Nets, a parametric and compact extension of the Petri Net formalism, with FST-PSO, an efficient and settings-free meta-heuristics for global optimization that is suitable for the PE problem. To prove the effectiveness of our modeling and calibration approach, we investigate here a large-scale kinetic model of human intracellular metabolism. To efficiently execute the large number of simulations required by PE, we exploit LASSIE, a deterministic simulator that offloads the calculations onto the cores of Graphics Processing Units, thus allowing a drastic reduction of the running time. Our results attest that estimating isoform-specific kinetic parameters allows to predict how the knock-down of specific enzyme isoforms affects the dynamic behavior of the metabolic network. Moreover, we show that, thanks to LASSIE, we achieved a speed-up of ~30× with respect to the same analysis carried out on Central Processing Units